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WO2025137739A1 - Procédé et appareil destinés à déterminer des paramètres de séquestration - Google Patents

Procédé et appareil destinés à déterminer des paramètres de séquestration Download PDF

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Publication number
WO2025137739A1
WO2025137739A1 PCT/AT2024/060165 AT2024060165W WO2025137739A1 WO 2025137739 A1 WO2025137739 A1 WO 2025137739A1 AT 2024060165 W AT2024060165 W AT 2024060165W WO 2025137739 A1 WO2025137739 A1 WO 2025137739A1
Authority
WO
WIPO (PCT)
Prior art keywords
sequestration
parameters
parameter
test
storage material
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/AT2024/060165
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German (de)
English (en)
Inventor
Lukas HÖBER
Gero Schwarz
Roberto LERCHE
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sequestra Flexco
Original Assignee
Sequestra Flexco
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sequestra Flexco filed Critical Sequestra Flexco
Publication of WO2025137739A1 publication Critical patent/WO2025137739A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/34—Chemical or biological purification of waste gases
    • B01D53/46—Removing components of defined structure
    • B01D53/62—Carbon oxides
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
    • B01D53/34—Chemical or biological purification of waste gases
    • B01D53/346—Controlling the process
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D2257/00—Components to be removed
    • B01D2257/50—Carbon oxides
    • B01D2257/504—Carbon dioxide

Definitions

  • the present invention relates, inter alia, to a method, a device, and a machine-readable storage medium according to the independent patent claims.
  • the present invention relates to the technical field of carbon dioxide sequestration.
  • One object of the invention can therefore be seen in creating a method and a device capable of determining optimized operating parameters of a sequestration process with the least possible effort.
  • the determination should be possible on a laboratory scale, such as in a mobile system.
  • different input parameters can be derived by examining a storage material, which are then subjected to processing steps, such as data processing in a computer-aided self-learning model and/or a regression model, to provide a parameter set with optimized sequestration parameters.
  • processing steps such as data processing in a computer-aided self-learning model and/or a regression model, to provide a parameter set with optimized sequestration parameters.
  • the result i.e., the determined parameter set, can subsequently be used to operate an industrial-scale sequestration plant with these optimized parameters.
  • the term "storage material" within the meaning of this description generally encompasses all materials that have at least one carbonatable phase.
  • the storage material is a solid, in particular a mineral solid.
  • the storage material can, for example, be a residual material from an industrial primary process, such as slag.
  • At least one material parameter of the storage material, also referred to as the starting material can be determined in one step.
  • the material parameter is, in particular, an inherent or unchangeable material parameter, such as the chemical composition or the mineralogical composition.
  • the material parameter can be determined using an analysis device.
  • the analysis device can be arranged or carried out spatially remote from other parts of the device or steps of the method. For example, the analysis can be performed directly at the source of the storage material, while other steps are carried out at a different location.
  • the determined material parameter is representative of the entirety or at least of a certain proportion of the storage material.
  • the analysis device can, for example, use one or more of the following analysis techniques: X-ray fluorescence analysis (XRF); optical emission spectroscopy (OES), in particular inductively coupled plasma optical emission spectroscopy (ICP-OES); mass spectrometry (MS), in particular inductively coupled plasma mass spectrometry (ICP-MS). If different material parameters are to be determined, the analysis device can also comprise several, possibly independent, analysis devices.
  • XRF X-ray fluorescence analysis
  • OES optical emission spectroscopy
  • ICP-OES inductively coupled plasma optical emission spectroscopy
  • MS mass spectrometry
  • ICP-MS inductively coupled plasma mass spectrometry
  • the analysis device can be configured to determine the desired material parameter directly from a solid (e.g., XRF) or from a processed, e.g., dissolved, form of the storage material (e.g., ICP-OES).
  • a solid e.g., XRF
  • a processed e.g., dissolved, form of the storage material (e.g., ICP-OES).
  • sequestration experiments can be conducted on a laboratory scale.
  • a sequestration experiment can be conducted in a sequestration reactor, for example, a laboratory sequestration reactor.
  • the measuring device intended to determine a sequestration parameter can be located at or remote from the site where the sequestration tests are carried out.
  • the determined sequestration parameter can subsequently be used to determine at least one target parameter. If necessary, several sequestration parameters can be combined into a single target parameter.
  • the number of target parameters can be equal to, greater than, or less than the number of determined sequestration parameters.
  • a data processing algorithm can be applied to the respective sequestration parameter(s).
  • a target parameter is typically indicative of a sequestration property of the storage material, for example, the CO2 storage potential.
  • a prediction data set can be stored in a memory unit, which can also be part of a computer.
  • the prediction data set can include at least the material parameter(s); the sequestration parameter(s); and the target variable(s).
  • the target variables are assigned, in particular, to the sequestration parameters of the experimental parameter sets.
  • a parameter set can be output or obtained as a result, which contains or consists of sequestration parameters optimized with regard to the target variable.
  • This parameter set can be used to operate a sequestration plant on an industrial scale. If necessary, further adjustments can be made to the parameter set to adapt the parameters to the specific conditions of the sequestration plant.
  • the following designations may be used for the parameters and other values mentioned here:
  • each experimental parameter set comprises one or more predetermined sequestration parameters
  • Sequestration characteristic Seq-i, Seq2, Seq n where n is the number of sequestration characteristics determined
  • Target variable Z1 , Z2, ... , Z P where p is the number of target variables determined from the sequestration parameters
  • step e) the target variable Z1, Z2, ... , Z P corresponds directly to a sequestration parameter Seqi, Seq2, ... , Seq n .
  • a machine-readable storage medium which comprises computer-executable instructions for carrying out a method described herein.
  • the device comprises one or more of the following components: a) an analysis device for determining at least one material parameter Mi, M2, Mk of the storage material, b) a test sequestration device for carrying out a plurality of sequestration tests on a test sample of the storage material, wherein each sequestration test is carried out with a predetermined test parameter set Pi, P2, ... , Pm, and wherein the test parameter sets Pi, P2, ...
  • Pm of the sequestration tests differ from one another in at least one sequestration parameter
  • a measuring device for determining a plurality of sequestration parameters Seqi, Seq2, ... , Seq n from the test samples
  • a computing unit for determining at least one target variable Z1, Z2, ... , Z P from the sequestration parameters Seqi, Seq2, ... , Seq n
  • a storage unit for storing a storage material-specific Prediction data set, wherein the prediction data set comprises material parameters Mi, M2, ... , Mk, sequestration parameters of the test parameter sets Pi, P2, ... , Pm and target variable Z1, Z2, ... , Z P , f) an electronic output unit,
  • the computing unit applies a prediction model to the prediction data set and optimizes the sequestration parameters with regard to the target variable Z1, Z2, ..., Z P , whereby the electronic output unit outputs a parameter set P op t with optimized sequestration parameters.
  • the analysis device may comprise one or more of the following devices: a spectroscopic device, in particular an X-ray spectroscopic device; a mass spectrometric device; an emission spectrometric device.
  • the measuring device comprises one or more of the following devices: a temperature sensor; a pressure sensor; a device for measuring the concentration of at least one gas, in particular Carbon dioxide; a flow meter; a balance; a viscometer; a pH sensor; a thermogravimetric analysis (TGA) device.
  • the device comprises a pre-processing device for treating a test sample to adjust a variable material parameter, wherein the pre-processing device comprises, for example, a grinding device and/or a drying or moistening device.
  • the computing unit, the storage unit and the electronic output unit are part of a computer.
  • the device is a mobile device and/or that the components of the device are accommodated in a common housing.
  • the invention also relates to a method for operating a plant for the sequestration of carbon dioxide in a storage material, wherein the plant is operated with an optimized parameter set P op t obtained in a method described here.
  • the optimized parameter set Popt is transmitted to the system via the electronic output unit.
  • Fig. 1 shows a schematic representation of the sequence of a method according to an embodiment.
  • Fig. 1 analysis device 1; test sample 2; storage material 3; test sequestration device 4; electronic output unit 5; measuring device 6; computing unit 7; storage unit 8; pre-processing device 9; computer 10; industrial sequestration plant 11.
  • the storage material 3, which is slag from an industrial process is analyzed using an analysis device 1.
  • the analysis device 1 is designed as an X-ray fluorescence analysis device and is configured to determine the chemical composition of the storage material 3. From this, the contents of carbonatable phases can be determined.
  • the analysis device 1 determines the contents of CaO, MgO, and Fe2O3 in the storage material. These contents form the material parameters Mi, M2, and M3, which, as illustrated in Fig. 1, are transmitted to the computing unit 7 and temporarily stored in a prediction data set V in the storage unit 8 until further processing.
  • test samples 2 are formed from the storage material 3, each of which is subjected to a sequestration test in the test sequestration device 4.
  • Each sequestration test is assigned a test parameter set P1, P2, P3, P4, P5, with the test parameter sets differing from each other in at least one sequestration parameter.
  • the test parameter sets with their sequestration parameters are transmitted to the computing unit 7 and temporarily stored in the prediction data set V.
  • the sequestration parameters considered in the present embodiment are: temperature T; pressure p; grain size of the storage material d; carbon dioxide content in the gas c(CO2).
  • a mill is provided as pre-processing device 9 to adjust, i.e., reduce, the sequestration parameter, the grain size of the storage material d.
  • the sequestration parameter, the grain size of the storage material d is left in its initial state.
  • the mass increase of the test samples 2 is continuously monitored using measuring device 6, which comprises several scales, and the temporal progression of the measured values is recorded.
  • the temporal progression of the mass represents the first sequestration parameter Seqi.
  • Further tests are carried out on the sequestered test samples using measuring device 6.
  • Further sequestration parameters determined in measuring device 6 include CO2 binding stability (Seq2), the eluate behavior of heavy metals (Seqs), and the mechanical strength (Seq4).
  • the results obtained for the sequestration parameters are forwarded to the computing unit 7, where they are summarized into two target variables Z1 and Z2 using a predefined calculation algorithm.
  • the target variables Z1 and Z2 are indicative of the CO2 storage potential and the reusability of the storage material 3.
  • the target variables are stored in the prediction data set V.
  • the prediction data set V stored in the storage unit 8 thus comprises the material parameters Mi, M2 and M3, the test parameter sets Pi, P2, P3, P4 each with four sequestration parameters (T1-4; p-1-4; di-4; c(CO2)i-4) as well as the target variables Z1 and Z2 for each test parameter set.
  • a prediction model is then applied to the prediction data set V in the computing unit 7, in which the sequestration parameters are optimized with regard to the target variables Z1 and Z2.
  • the prediction model is a self-learning model that was trained using a plurality of training data sets T.
  • the training data sets T like the prediction data set V, comprise the material parameters Mi, M2, and M3, a plurality of experimental parameter sets Pi, P2, ..., Pm, each with four sequestration parameters (Ti- m ; pi-m; di- m ; c(C02)im), as well as the target variables Z1 and Z2 for each experimental parameter set.
  • Each training data set refers to a different storage material.
  • the result output via an electronic output unit 5 consists of an optimized parameter set P op t, which contains the optimized sequestration parameters temperature Topt; pressure p op t; grain size of the storage material dopt; carbon dioxide content in the gas c(CO2)o P t.
  • the electronic output unit 5 is designed as an interface to an industrial sequestration system 11, to which the parameter set Popt is forwarded.
  • an optimized sequestration process can be carried out on the storage material 3 in the industrial sequestration system 11.
  • the carbon dioxide introduced into this sequestration process can, for example, originate from another industrial plant.
  • the data of the prediction dataset V can be used as training dataset T in the self-learning model.
  • the electronic output unit 5, the computing unit 7 and the storage unit 8 are part of a computer 10.
  • the optimization is performed with one iteration.
  • five optimized parameter sets P opti -5 are first determined, which are used as experimental parameter sets in a further run of the sequestration experiments.
  • the results obtained are processed as already described in the above embodiment in order to obtain an optimized parameter set Popt after one iteration.

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Oil, Petroleum & Natural Gas (AREA)
  • General Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Environmental & Geological Engineering (AREA)
  • Evolutionary Computation (AREA)
  • Automation & Control Theory (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Treating Waste Gases (AREA)

Abstract

La présente invention concerne un procédé mis en œuvre par ordinateur et un appareil destinés à déterminer des paramètres de fonctionnement optimisés d'une installation pour la séquestration de dioxyde de carbone dans un matériau de stockage, dans un dispositif de séquestration de test (4), de multiples tests de séquestration étant réalisés sur chaque échantillon de test (2), chaque test de séquestration étant réalisé au moyen d'un ensemble de paramètres de test spécifié, et un modèle de prédiction étant appliqué à l'enregistrement de données de prédiction au moyen d'une unité de calcul (7), ce qui permet d'optimiser les paramètres de séquestration.
PCT/AT2024/060165 2023-12-29 2024-04-23 Procédé et appareil destinés à déterminer des paramètres de séquestration Pending WO2025137739A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
AT602432023 2023-12-29
AT60243/2023 2023-12-29
AT60244/2023 2023-12-30
AT602442023 2023-12-30

Publications (1)

Publication Number Publication Date
WO2025137739A1 true WO2025137739A1 (fr) 2025-07-03

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Application Number Title Priority Date Filing Date
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WO (1) WO2025137739A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200009499A1 (en) * 2017-03-06 2020-01-09 Ion Engineering, Llc Carbon dioxide capture system and spectroscopic evaluation thereof
WO2021188961A1 (fr) * 2020-03-20 2021-09-23 Michael Smith Procédés d'analyse de dioxyde de carbone sous la surface
CN114862028A (zh) * 2022-05-17 2022-08-05 中国华能集团清洁能源技术研究院有限公司 二氧化碳封存性能预测方法、装置及存储介质
CN116702621A (zh) * 2023-06-25 2023-09-05 苏州碳位科技有限公司 用于co2矿化流程中固碳量核算的方法、服务器及介质
CN117875167A (zh) * 2023-12-27 2024-04-12 中国石油天然气集团有限公司 一种烟气co2捕集装置工艺参数的优化方法、装置和设备

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200009499A1 (en) * 2017-03-06 2020-01-09 Ion Engineering, Llc Carbon dioxide capture system and spectroscopic evaluation thereof
WO2021188961A1 (fr) * 2020-03-20 2021-09-23 Michael Smith Procédés d'analyse de dioxyde de carbone sous la surface
CN114862028A (zh) * 2022-05-17 2022-08-05 中国华能集团清洁能源技术研究院有限公司 二氧化碳封存性能预测方法、装置及存储介质
CN116702621A (zh) * 2023-06-25 2023-09-05 苏州碳位科技有限公司 用于co2矿化流程中固碳量核算的方法、服务器及介质
CN117875167A (zh) * 2023-12-27 2024-04-12 中国石油天然气集团有限公司 一种烟气co2捕集装置工艺参数的优化方法、装置和设备

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